Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean
Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accession...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.966244/full |
| _version_ | 1811250888249966592 |
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| author | Ashlyn Rairdin Fateme Fotouhi Fateme Fotouhi Jiaoping Zhang Daren S. Mueller Baskar Ganapathysubramanian Asheesh K. Singh Somak Dutta Soumik Sarkar Soumik Sarkar Arti Singh |
| author_facet | Ashlyn Rairdin Fateme Fotouhi Fateme Fotouhi Jiaoping Zhang Daren S. Mueller Baskar Ganapathysubramanian Asheesh K. Singh Somak Dutta Soumik Sarkar Soumik Sarkar Arti Singh |
| author_sort | Ashlyn Rairdin |
| collection | DOAJ |
| description | Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms. |
| first_indexed | 2024-04-12T16:11:28Z |
| format | Article |
| id | doaj.art-069e944f59ec48798ddd2308075ba99d |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T16:11:28Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-069e944f59ec48798ddd2308075ba99d2022-12-22T03:25:53ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-10-011310.3389/fpls.2022.966244966244Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybeanAshlyn Rairdin0Fateme Fotouhi1Fateme Fotouhi2Jiaoping Zhang3Daren S. Mueller4Baskar Ganapathysubramanian5Asheesh K. Singh6Somak Dutta7Soumik Sarkar8Soumik Sarkar9Arti Singh10Department of Agronomy, Iowa State University, Ames, IA, United StatesDepartment of Mechanical Engineering, Iowa State University, Ames, IA, United StatesDepartment of Computer Science, Iowa State University, Ames, IA, United StatesDepartment of Agronomy, Iowa State University, Ames, IA, United StatesDepartment of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United StatesDepartment of Mechanical Engineering, Iowa State University, Ames, IA, United StatesDepartment of Agronomy, Iowa State University, Ames, IA, United StatesDepartment of Statistics, Iowa State University, Ames, IA, United StatesDepartment of Mechanical Engineering, Iowa State University, Ames, IA, United StatesDepartment of Computer Science, Iowa State University, Ames, IA, United StatesDepartment of Agronomy, Iowa State University, Ames, IA, United StatesUsing a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.https://www.frontiersin.org/articles/10.3389/fpls.2022.966244/fullstress phenotypingdisease quantificationobject detectionforeground detectionROC analysisimage-based phenotyping |
| spellingShingle | Ashlyn Rairdin Fateme Fotouhi Fateme Fotouhi Jiaoping Zhang Daren S. Mueller Baskar Ganapathysubramanian Asheesh K. Singh Somak Dutta Soumik Sarkar Soumik Sarkar Arti Singh Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean Frontiers in Plant Science stress phenotyping disease quantification object detection foreground detection ROC analysis image-based phenotyping |
| title | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| title_full | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| title_fullStr | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| title_full_unstemmed | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| title_short | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| title_sort | deep learning based phenotyping for genome wide association studies of sudden death syndrome in soybean |
| topic | stress phenotyping disease quantification object detection foreground detection ROC analysis image-based phenotyping |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.966244/full |
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